تخمین عیار کانسار فسفات اسفوردی با روش رگرسیون بردار پشتیبان

[1]     Dutta, S. (2006). “Predictive performance of machine learning algorithms for ore reserve estimation in sparse and imprecise data”. PhD thesis, University of Alaska Fairbanks, pp. 189.

[2]     Hassani pak, A. A., and Sharafodin, M. (2001). “Exploration Data Analysis”. Tehran University Press, pp. 991.

[3]     Dutta, S., Bandopadhyay, S., Ganguli, R., and Misra, D. (2010). “Machine Learning Algorithms and Their Application to Ore Reserve Estimation of Sparse and Imprecise Data”. Journal of Intelligent Learning Systems & Applications, 2: 86-96.

[4]     Alpaydin, E. (2010). “Introduction to Machine Learning”. Second Edition, Cambridge, Mass.: MIT Press, pp. 584.

[5]     Nilsson, N. J. (1998). “Introduction to Machine Learning”. An Early Draft of a Proposed Textbook, Department of Computer Science, Stanford University, Stanford, CA 94305, pp. 188.

[6]     Samanta, B., Ganguli, R., and Bandopadhyay, S. (2005). “Comparing the predictive performance of neural networks with ordinary kriging in a bauxite deposit”. Mining Technology, 114: 129-139.

[7]     Chatterjee, S., Bhattacherjee, A., Samanta, B., and Pal, S. K. (2006). “Ore Grade Estimation of a Limestone Deposit in India Using an Artificial Neural Network”. Applied GIS, 2: 1-20.

[8]     Chatterjee,S., Bandopadhyay, S., and Machuca, D. (2010). “Ore Grade Prediction Using a Genetic Algorithm and Clustering Based Ensemble Neural Network Model”. Math Geosci, 42: 309–326.

[9]     Guo, W. W. (2010). “A novel application of neural networks for instant iron-ore grade estimation”. Expert Systems with Applications, 37(12): 8729-8735.

[10]  Li, X-l., Xie, Y-l., Guo, Q-j., and Li, L-h. (2010). “Adaptive ore grade estimation method for the mineral deposit evaluation”. Mathematical and Computer Modelling, 52(11-12): 1947-1956.

[11]  Tahmasebi, P., and Hezarkhani, A. (2010). “Application of Adaptive Neuro-Fuzzy Inference System for Grade Estimation; Case Study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran”. Australian Journal of Basic and Applied Sciences, 4(3): 408-420.

[12]  Tahmasebi, P., and Hezarkhani, A. (2012). “A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation”. Computers & Geosciences,42: 18–27.

[13]  Mahmoudabadi, H., Izadi, M., and Menhaj, M. B. (2009). “A hybrid method for grade estimation using genetic algorithm and neural networks”. Computational Geosciences, 13: 91-101.

[14]  Nezamolhosseini, S. A., Mojtahedzadeh, S. H., and Gholamnejad, J. (2017). “The Application of Artificial Neural Networks to Ore Reserve Estimation at Choghart Iron Ore Deposit”. Analytical and Numerical Methods in Mining Engineering, 6(Special Issue): 73-83.

[15]  صیادی، ا. ر.، منجزی، م.، شهرآبادی، ح.؛  1388؛"برآورد ذخیره کانسار فسفات اسفوردی با استفاده از روشهای زمین آماری و شبکه عصبی مصنوعی". فصلنامه علوم زمین، دوره هجدهم، شماره 69، ص 102-109.

[16]  Jafrasteh, B., Fathianpour, N., and Suárez, A. (2018). “Comparison of machine learning methods for copper ore grade estimation”. Computational Geosciences, 22: 1371-1388.

[17]  Matías, J. M., Vaamonde, A., Taboda, J., and González-Manteiga, W. (2004). “Support vector machines and gradient boosting for graphical estimation of a slate deposit”. Stochastic Environmental Research and Risk Assessment, 18: 309-323.

[18]  Tenorio, V. O.,  Bandopadhyay, S., Misra, D., Naidu, S., and Kelley, J. (2015). “Support Vector Machines Applied for Resource Estimation of Underwater Glacier-Type Platinum Deposits”. Society for Mining, Metallurgy, and Exploration, 889-902.

[19]  Maleki, S., Ramazia, H. R., and Moradi, S. (2014). “Estimation of Iron concentration by using a support vector machineand an artificial neural network - the case study of the Choghart deposit southeast of Yazd, Yazd, Iran”. Geopersia, 4: 201-212.

[20]  Bakhshandeh Amnieh, H., Siamaki, A., and Soltani, S. (2012). “Design of blasting pattern in proportion to the peak particle velocity (PPV): Artificial neural networks approach”. Safety Science,  50: 1913–1916.

[21]  Kotake, N., Suzuki, K., Asahi, S., and Kanda, Y. (2002). “Experimental study on the grinding rate constant of solid materials in a ball mill”. Powder Technology, 122: 101–108.

[22]  Singh, V., Banerjee, P. K., Tripathy, S. K., Saxena, V. K., and Venugopal, R. (2013). “Artificial Neural Network Modeling of Ball Mill Grinding Process”. Powder Metallurgy & Mining, 2(2): 2-4.

[23]  Braspenning P. J., and Thuijsman F. (1995). “Artificial Neural Networks: An Introduction to ANN Theory and Practice”. Springer Science & Business Media, New York, pp. 295.

[24]  Miller, D. M., Kaminsky, E. J., and Rana, S. (1995). “Neural Network Classification of Remote Sensing Data”. Computers & Geosciences, 21: 377-386.

[25]  Wang, Y. G., and Li, H. P. (2010). “Remote sensing image classification based on artificial neural network: A case study of Honghe Wetlands National Nature Reserve”. International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), 17-20.

[26]  Lee, C., and Sterling, R. (1992). “Identifying probable failure modes for underground openings using a neural network”. International Journal of Rock Mechanics and Mining Science & Geomechanics Abstracts, 29(1): 49-67.

[27]  Shahin, M. A., Jaksa, M. B., and Maier, H. R. (2008). “State of the art of artificial neural networks in geotechnical engineering”. Electronic Journal of Geotechnical Engineering, 8: 1-26.

[28]  Kecman, V. (2000). “Learnig and Soft Computing: Support Vector Machines, Neural Network and Fuzzy Logic Models”. MIT Publishers, pp. 576.

[29]  Kecman, V. (2004). “Support Vector Machines Basics”. The University of Auckland, School of Engineering, Report 616, pp. 58.

[30]  Smola, A. J., and Scholkopf, B. (1998). “A Tutorial on Support Vector Regression”. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK.

[31]  Smola, A. J., and Scholkopf, B. (2004). “A Tutorial on Support Vector Regression”. Statistics and Computing, 14: 199-222.

[32]  Sugumaran, V., and Ramachandran, K. I. (2011). “Effect of number of features on classification of roller bearing faults using SVM and PSVM”. Expert Systems with Applications, 38: 4088–4096.

[33]  Zhen-yuan, J., Jian-wei, M., Fu-ji, W., and Wei, L. (2011). “Hybrid of simulated annealing and SVM for hydraulic valve characteristics prediction”. Expert Systems with Applications, 38: 8030–8036.

[34]  Pozdnoukhov, A. (2005). “Support Vector Regression for automated Robust Spatial Mapping of Neural Radioactivity”. Journal of Applied GIS, 1(2): 1-10.

[35]  Twarakavi, N. C., Misra, D., and Bandopadhayay, S. (2006). “Prediction of Arsenic in Bedrock Derived Stream Sediments at a Gold Mine Site under Conditions of Sparse Data”. Natural Resources Research, 15: 15-26.

[36]  Abbaszadeh, M., Hezarkhani, A., and Soltani-Mohammadi, S. (2013). “An SVM based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit”. Chem Erde-Geochemistry, 73: 545-554.

[37]  Abbaszadeh, M., Hezarkhani, A., and Soltani-Mohammadi, S. (2015). “Classification of Alteration Zones Based on Whole- Rock Geochemical Data using Support Vector Machine”. Journal of the Geological Society of India, 85: 500-508.

[38]  Soliman, O. S., and Mahmoud, A. S. (2012). “A classification system for remote sensing satellite images using support vector machine with non-linear kernel functions”. 8th International Conference on Informatics and Systems (INFOS), Cairo, 181-187.

[39]  Soliman, O. S., Mahmoud, A. S., and Hassan, S. M. (2012). “Remote Sensing Satellite Images Classification Using Support Vector Machine and Particle Swarm Optimization”. Third International Conference on Innovations in Bio-Inspired Computing and Applications, Kaohsiung, 280-285.

[40]  Manthira Moorthi, S., Misra, I., Kaur, R., Darji, N. P., and Ramakrishnan, R. (2011). “Kernel based learning approach for satellite image classification using support vector machine”. IEEE Recent Advances in Intelligent Computational Systems, Trivandrum, 107-110.

[41]  رجب زاده، م. ع.، حسینی، ک.، موسوی نسب، ز.؛ 2015؛ "مطالعه کانی‌شناسی و زمینشیمی آپاتیت و سنگ‌های میزبان فسفات در کانسار اسفوردی استان یزد با هدف تعیین منشأ و خاستگاه آپاتیت". زمین شناسی اقتصادی، دورهششم، شماره 2، ص 331-353.

[42]  سهیلی، م.، مهدوی، م.؛ 1370؛"نقشه زمین ‌شناسی اسفوردی، مقیاس 1:100000". سازمان زمین ‌شناسی کشور.

[43]  شهرابی، ج.، ذوالقدرشجاعی، ع.؛ 1390؛"داده ‌کاوی پیشرفته: مفاهیم و الگوریتم‌ها". انتشارات جهاد دانشگاهی واحد دانشگاه صنعتی امیرکبیر، 472 صفحه.

[44]  Zhang, D., Liu, W., Wang, A., and Deng, Q. (2011). “Parameter Optimization for Support Vector Regression Based on Genetic Algorithm with Simplex Crossover Operator”. Journal of Information & Computational Science, 8: 911-920.

[45]  Zuo, R., and Carranza, M. E. J. (2011). “Support vector machine: A tool for mapping mineral prospectivity”. Computers & Geosciences, 37: 1967–1975.

[46]  Matheron, G. (1969). “Le krigeage universel (Universal kriging)”. Cahiers du Centre de Morphologie Mathematique, Ecole des Mines de Paris, Fontainebleau, 1: pp. 83.

[47]  Christensen, R. (1991). “Linear models for multivariate, time series, and spatial data”. Springer Science & Business Media, New York, pp. 318.

[48]  Oliver, M. A., and Webster, R. (2014). “A tutorial guide to geostatistics: Computing and modelling variograms and kriging”. CATENA, 113: 56-69.

[49]  Journel, A., and Huijbregts, C. J. (1978). “Mining geostatistics”. Academic Press, London, New York, pp. 600.

[50]  Webster, R., and Oliver, M. A. (2007). “Geostatistics for Environmental Scientists”. 2nd Edition, John Wiley & Sons, Chichester, pp. 330.

[51]  Diggle, P. J., Tawn, J. A., and Moyeed, R. (2007). “Model-based geostatistics”. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(3): 299-350.

[52]  Brown, P. (2015). “Model-Based Geostatistics the Easy Way”. Journal of Statistical Software, 63(12): 1-24.

[53]  Lin, S. W., Lee, Z. J., Chen, S. C., and Tseng, T. Y. (2008). “Parameter determination of support vector machine and feature selection using simulated annealing approach”. Applied Soft Computing, 8: 1505-1512.

[54]  Che, X. L., and Hu, L. (2008). “Grid Resource Prediction Approach Based On Nu-Support Vector Regression”. Proceedings of the 7th International Conference on Machine Learning and Cybernetics, Kunming, 12-15 July. 

[55]  Hsu, C. W., Chang, C. C., and Lin, C. J. (2010). “A Practical Guide to Support Vector Classification”. Technical Report, Department of Computer Science and Information Engineering, University of National Taiwan, Taipei, 1-12.

[56]  Chang, C. C., and Lin, C. J. (2011). “LIBSVM: a library for support vector machines”. ACM Transactions on Intelligent Systems and Technology, 2: 1-27.